Reminds me of when translation software first started becoming popular, the advice was to translate the text then translate it back to see if it still made sense.
One I saw of this recently was English "cologne" -> Spanish "colonia" -> English "colony". It was in a Google-Translate-Sings version of "bad guy" by Billie Eilish
Here's an idea: Train a GANN to convert to and from Disney art style, to real life. Then Disney can use it to create a live action remake with basically no budget. Instant profit!
The application for MR to CT is radiation therapy. The soft tissue contrast from MRI is often used to determine the target region, but the CT carries the radio-density information needed for determining the radiation therapy dose distribution plan. Instead of taking both MRI and CT and registering them, the synthetic CT from MRI lets you get the job done without the extra time, expense, and radiation dose of the CT.
Oh finally! I've got all these horse pictures left to do and the kindergarten kids are really bad at doing proper shadows, especially around the neck. You're a life saver!
One part of this that I'd like to know more about is the final, round-trip comparison. I'm sure that calculating the 'distance' between original and round-trip image involves something more sophisticated than a pixel-by-pixel comparison.
Fun fact its actually not. There's a few more things that go into 'telling' the generator how it did, but the cycle loss is literally just finding the average difference between each pixel
Great video ! Especially the discussion about how reliable or trustworthy super-resolution can be when using a single image. If the information is not captured by the camera, there's nothing you can do about it. You need some extra knowledge about what you're looking at, like for instance more low resolution pictures.
Yes. It is pretty sad how most peoples scientific knowledge and understanding comes from the TV and films. Even Sci Fi films. Though in this instance I think CSI could be as or more responsible. But many people really do not understand. They really do think a computer can somehow magically come up with unknown information and apply it to an image to enhance it to a much higher resolution, in order to then extract actual information from the picture. But they have seen it happen on TV. So it must be possible. I have had this very argument on more than one occasion.
There were cases where the GAN that transforms Horses to Zebras cryptographically encoded the original Horse image into the Zebra image, so that the second GAN can then easily reproduce the original image.
huh “hiding information” in the encoding like that sounds interesting! However, if you not only did H->Z->H but also Z->H->Z, and those come from real images with random nonsense for that, how does it “know” whether it should interpret those or not?
@@AlexKnauth I think most likely it would just interpret what it thinks is the hidden info, resulting a some small random noise. Check out the original paper "CycleGAN, a Master of Steganography" by Chu et al.
My favourite CycleGAN fail story is one that was made to take areal photos and turn into maps. It made nonsense maps and was suspiciously good at recreating lamp posts, fans on top of buildings, and so on. Turns out it did make nonsense maps, then hid information about the original image in the least significant bits of the pixels in those maps. I don't remember where I read about it, though. So you might have to take its truth with a grain of salt.
MasterHigure huh, that “hiding information in the least significant bits” thing sounds interesting! However, if you not only did H->Z->H but also Z->H->Z, and those come from real images with random nonsense for those bits, how does it “know” whether it should interpret those or not?
Oh, another commenter named Karl Kastor also mentoined this. They pointed me to a paper called "CycleGAN, a Master of Steganography" by Chu et al. Is that where you read about this?
I wonder if it is possible to train these systems to undo image processing effects such as a Gaussian blur, and if so how accurate they could be since you could easily create a huge number of paired data points? I know that there are inverse Gaussian blur equations that can be used but these do create a certain amount of error, so it would be interesting to compare the two approaches to see which could produce the most accurate results.
This reminded me of the "game" of translating a text, translating it back and laughing at the differences. Could CycleGAN help improve machine translations?
DB Fuller oh yeah maybe. Like one I say recently was English “cologne” -> Spanish “colonia” -> English “colony”. The CylcleGan would have to encode the meaning of “cologne” into it in a way that didn’t lose the information that it wasn’t a colony, somehow.
Possibly, though GANs up until now have been better at images than languages. I imagine the idea of having another network actively undoing the work of the first network could help a lot, though.
So I can see how the loss functions for the individual images would be applied to their respective networks, but how would the loss function between the two horse images be applied? To each network separately? All together as one big system?
Also, why having two discriminators? If you are comparing the output horse with the input horse, you are already penalising the network if it cannot reproduce the original (real) picture. I understand the one in the middle, not the last one
Yeah that's what I don't understand, it's there to prevent mode collapse but that problem originates from the 1st generator. I'm curious how you would go about applying that loss function to the generator. The only thing I can think is that you would preform backprop and start from the 2nd discriminator and work all the way back to the 1st generator and use those gradients to update the 1st generator. But I have no idea if that's actually how its done.
So because both GANs are used during a full cycle, horse -> fake_zebra uses gan A and fake_zebra -> cycle_horse uses gan B, the loss between those images would be applied to both GANs, but not the discriminators. You need two discriminators because each one is applied to one GAN. GAN A will need a dedicated discriminator to take the fake zebra it produces and the real zebra. The discriminator here scores both of these individually, giving a higher score if it thinks its a 'real zebra'. We also need a discriminator to do this for knowing what a 'real horse' looks like as well for GAN B, hence two discriminators.
But isn’t “zebrafying” a horse a lossy process? What I mean is if you convert a brown horse to a zebra and you convert a white horse of the exact same size to a zebra they should produce the same result. But then how will the inverse function know what color to turn it back into?
Ankith Udupa I would like to know this as well. I wonder if it would try to “hide” the encoded information of the horse color in something like the placement of the stripes? But then when the inverse network is given a real Zebra that information wouldn’t be there... would it come out as a nonsense horse-color? Another commenter said a similar thing about lossy process with converting to a Monet-painting
Another commenter named Karl Kastor also mentioned something like this. They pointed me to a paper called "CycleGAN, a Master of Steganography" by Chu et al. I thought that was interesting, it used conventing aerial photos -> maps as an example of a lossy process, and showed that adding tiny imperceptible random noise to the map changed the aerial photos dramatically, so somehow it was encoding that "lost" information where it thought we wouldn't notice
One generator is predicting the input image, and the other is predicting the zebra / modified input image. So I think whatever we are reverse-predicting is just whatever the input image is. The wrong color would have a high loss.
If one were to convert to a monet painting then it would be lossy so wouldn't a cyclegan incentivise it to be a bit less like a monet so that there is enough data to covert it back to the original? If the network gives you the same zebra every time but places the original, shrunk down and in the corner then the reverse network can just take that corner and expand it? Does that ever happen?
First question: That is probably true and depends on how you weight the two losses. Second question: A similar thing like what you said actually happened: The network stenographically encoded the original input into the Zebra image and then the reverse network could decode this.
Karl Kastor what happens when the reverse network is given a real Zebra image? or a real Monet painting is the more interesting case. The reverse network might be looking for encoded information that isn’t there, so would it be nonsense data when it tries to interpret it?
@@AlexKnauth It might not stenographically hide the whole image, but just some parts that need to be changed. But these parts would be random if the hidden info is not there, yeah. Check out the paper "CycleGAN, a Master of Steganography" by Chu et al. for more info
I saw NASA pull out a licence plate number from a few pixels by tracking the differences between frames on a movie and back calculating what the image must have been to produce that. Maybe combining that idea with this one can get a better 2001.
@@lucidmoses yes. If its a video it can be sampled in deepth. This increase the virtual resolution quite a bit There is software that used in cameras to take several puctueres to make s Sharp One.
Probably not. There's two options, horse or zebra. The discriminator can't say "neither" so it won't try to convince it that "neither" is the wrong option. If there's no zebra then it's likely to make the terrain look like it would be around a zebra. Like he mentioned in the video that the grass changes colour, probably because where zebras naturally live the grass is a different colour.
The real loss needs to calculate how the fake prodused is like intended product. I.e. photo to rembrant painting style, then how well the produced painting is similar to rembrant style..
I wonder what happens if you do it with very different images, like horses to oranges back to horses, or Computerfile videos to Numberfile images back to Computerfile videos.
For fixing the flickering: DVDs record the things which change between two consecutive images in a movie. Could we train an additional GAN to distinguish between such changes in normal movies (basically DVD data for real movies), perhaps even the original (style-unchanged) movie, and the changes which produce flickers in the style-changed product movies?
What if you could reduce images into its descriptive parts, then they could be removed from the image....each part can be compressed separately, then you have a higher ratio compression for video....and you can create internet 2.0...lol.
Absolutely. What's more to reach further and assume unicorn's should only be done based on pretty sound demonstrable evidence. Evidence that at least to my knowledge hasn't been remotely detected, let alone reasonably, scientifically demonstrated.
I guess the results could be improved a bit by adding a few free output neurons that are not part of the zebra image. These could be used by the first GAN to encode the information it destroys by turning a horse to a zebra. This could then be used by the second GAN to reconstruct a better image. It should improve the quality of the result because it gives more freedom to the first GAN to modify the image, but not so much that it could just throw a generic zebra.
Honestly I am not convinced that sort of enhancement is going to ever really be applicable for legal purposes precisely because it is always going to be guesswork at least unless you are dealing with an analogue source that does in fact have fine detail that is just too difficult for humans to make out. That said I can potentially see one are these could eventually find use in police work and that is perhaps as a far cheaper and thus more accessible way to go about creating images based on eyewitness descriptions etc, currently doing this is expensive as it requires a skilled human artist to spend a lot of time in an one on one consultation with a witness if it was possible to sit the witness down and talk to a computer to help it construct an image of the suspect this could probably be used in a lot more cases than it can now. Arguably this could potentially give even less potentially contaminated results than using a human too as a human can contaminate the witness testimony subconsciously due to their body language etc a computer couldn't even unintentionally influence the witness towards a desired outcome as it has no desires of it's own let alone non verbal cues by which to let any prejudicial expectations slip though it could do nothing more or less than do it's best to produce a satisfactory match for the image the witness is trying to describe.
That part in the beginning about commissioning artists to paint pictures for the GAN as direct examples... I've long had this idea that Hollywood needs to take digital cameras, and film cameras, and have them shoot at a variety of things in tandem, then run that data through a GAN, so we produce a system to make digital footage look like film footage, and rid ourselves of all the weird digital artifacts we see in films today. Mainly I'm just on a campaign to change the way digital photography handles bright coloured lights.
Dr. Pound shoulder shirt tug tally: 6. We need an algorithm to predict if the next video will have increasing or decreasing number of shoulder shirt tug twitches.
More like human versus neanderthal I imagine. Sadly we don't have a reference genome for zebra yet, so can't calculate a genetic distance between it and equus caballus. Humans spread around the world quickly in evolutionary terms.
Most brutal insult to artist: "An untrained GAN is better than you."
That is the best compliment.
i'd honestly be kind of embarrassed for the GAN, if an artist was better than it.
I’ll take that challenge
I'll start on the ai art critic and feed it disney and all episodes of the simpsons ....
Shows a total lack of understanding of art. Technical proficiency isnt what art is.
I see Dr Mike Pound, I watch
Please...can we just appreciate how clean Mike's board is?
Reminds me of when translation software first started becoming popular, the advice was to translate the text then translate it back to see if it still made sense.
Hence the likely apocryphal "The vodka is agreeable, but the meat has gone bad."
One I saw of this recently was English "cologne" -> Spanish "colonia" -> English "colony". It was in a Google-Translate-Sings version of "bad guy" by Billie Eilish
I like how he is even optimising the scribble time by painting the generator differently at 5:26 -before in 1:30 was slower.
Summer Clothes and Mike Pound = legend
Ambient temps srsly f*ing with his bioware.
Dr Mike Pound! This guy is the bessttt 😎
Here's an idea: Train a GANN to convert to and from Disney art style, to real life. Then Disney can use it to create a live action remake with basically no budget. Instant profit!
Shhh!! Why would you want to give them even _more_ money?!
I would wanna use it the opposite way. Take a live-action movie like Star Wars or something, and turn it into an animated movie!
Skynet is not an improvement over Disney, please, stop having ideas... ;)
That's not new; animated picture industry is actually actively dabbing into that kind of stuff. Please look forward to it.
The application for MR to CT is radiation therapy. The soft tissue contrast from MRI is often used to determine the target region, but the CT carries the radio-density information needed for determining the radiation therapy dose distribution plan. Instead of taking both MRI and CT and registering them, the synthetic CT from MRI lets you get the job done without the extra time, expense, and radiation dose of the CT.
Ah, you've said what I was going to say.
I don't care if I'm first or second or anything like that, just wanted to use this opportunity to tell that this channel is amazing!
It can also be used for 'Blind-Denoising' for cleaning EEG signals.
11:58 The algorythm zebraed the wooden pole on the left too 😂😂
Tommaso Morandini oh wow, Hahhaahahhha!
One of the best educational channels on TH-cam. Period.
I can recommend the 2001 SO Picasso version, it's amazing and should have been linked in the description.
Oh finally! I've got all these horse pictures left to do and the kindergarten kids are really bad at doing proper shadows, especially around the neck.
You're a life saver!
Dr. Pound is the best
One part of this that I'd like to know more about is the final, round-trip comparison. I'm sure that calculating the 'distance' between original and round-trip image involves something more sophisticated than a pixel-by-pixel comparison.
Fun fact its actually not. There's a few more things that go into 'telling' the generator how it did, but the cycle loss is literally just finding the average difference between each pixel
these days i just use the cyclegan to do voice conversion, what a coincidence to see this!
😊 Thank you for explaining this optimization-loop
Great video ! Especially the discussion about how reliable or trustworthy super-resolution can be when using a single image.
If the information is not captured by the camera, there's nothing you can do about it. You need some extra knowledge about what you're looking at, like for instance more low resolution pictures.
Yes. It is pretty sad how most peoples scientific knowledge and understanding comes from the TV and films. Even Sci Fi films. Though in this instance I think CSI could be as or more responsible.
But many people really do not understand. They really do think a computer can somehow magically come up with unknown information and apply it to an image to enhance it to a much higher resolution, in order to then extract actual information from the picture.
But they have seen it happen on TV. So it must be possible.
I have had this very argument on more than one occasion.
There were cases where the GAN that transforms Horses to Zebras cryptographically encoded the original Horse image into the Zebra image, so that the second GAN can then easily reproduce the original image.
These damn AI continuously working around the problem set... Almost human-like!
Can you link where you got that from?
huh “hiding information” in the encoding like that sounds interesting! However, if you not only did H->Z->H but also Z->H->Z, and those come from real images with random nonsense for that, how does it “know” whether it should interpret those or not?
@@AlexKnauth I think most likely it would just interpret what it thinks is the hidden info, resulting a some small random noise. Check out the original paper "CycleGAN, a Master of Steganography" by Chu et al.
@@karlkastor Oh, thank you!
Im going to be honest here, I will watch any video by this guy just to listen to his voice.
Excellent explanation❤
“We’ll put a link in the description”, he sounds like a regular youtuber now
and then there is no link
Dr Pound rocks.
GAN of Zebras could be an amazing story to tell children!
More zebras? I like this idea already.
Stuff about Generative Adversarial Networks? This a-GAN?!
(sorry)
get out lol
Don't be such a discriminator.
🍻 Thank You!
I need to get into this kind of thing.
I'd love to build one to remove telephone/power lines from my photos.
Finally I can fulfill my life-dream of knowing what kind of bread I am
Thanks BuzzFeed
basically you are assuring that F (and in return also G) are bijections, i.e. they don't "collapse" the domains
My favourite CycleGAN fail story is one that was made to take areal photos and turn into maps. It made nonsense maps and was suspiciously good at recreating lamp posts, fans on top of buildings, and so on.
Turns out it did make nonsense maps, then hid information about the original image in the least significant bits of the pixels in those maps.
I don't remember where I read about it, though. So you might have to take its truth with a grain of salt.
MasterHigure huh, that “hiding information in the least significant bits” thing sounds interesting! However, if you not only did H->Z->H but also Z->H->Z, and those come from real images with random nonsense for those bits, how does it “know” whether it should interpret those or not?
Oh, another commenter named Karl Kastor also mentoined this. They pointed me to a paper called "CycleGAN, a Master of Steganography" by Chu et al. Is that where you read about this?
I wonder if it is possible to train these systems to undo image processing effects such as a Gaussian blur, and if so how accurate they could be since you could easily create a huge number of paired data points? I know that there are inverse Gaussian blur equations that can be used but these do create a certain amount of error, so it would be interesting to compare the two approaches to see which could produce the most accurate results.
We want to see this done! Please show
Sooo can you convert a real Monet painting into a photograph?
can someone explain at 8:25, styling and content?
I love Computerphile
This reminded me of the "game" of translating a text, translating it back and laughing at the differences. Could CycleGAN help improve machine translations?
DB Fuller oh yeah maybe. Like one I say recently was English “cologne” -> Spanish “colonia” -> English “colony”. The CylcleGan would have to encode the meaning of “cologne” into it in a way that didn’t lose the information that it wasn’t a colony, somehow.
Possibly, though GANs up until now have been better at images than languages. I imagine the idea of having another network actively undoing the work of the first network could help a lot, though.
The most amazing thing for me is 5:25 instant improvement of drawing generators/that symbol!
So I can see how the loss functions for the individual images would be applied to their respective networks, but how would the loss function between the two horse images be applied? To each network separately? All together as one big system?
Also, why having two discriminators? If you are comparing the output horse with the input horse, you are already penalising the network if it cannot reproduce the original (real) picture. I understand the one in the middle, not the last one
Yeah that's what I don't understand, it's there to prevent mode collapse but that problem originates from the 1st generator. I'm curious how you would go about applying that loss function to the generator.
The only thing I can think is that you would preform backprop and start from the 2nd discriminator and work all the way back to the 1st generator and use those gradients to update the 1st generator. But I have no idea if that's actually how its done.
So because both GANs are used during a full cycle, horse -> fake_zebra uses gan A and fake_zebra -> cycle_horse uses gan B, the loss between those images would be applied to both GANs, but not the discriminators.
You need two discriminators because each one is applied to one GAN. GAN A will need a dedicated discriminator to take the fake zebra it produces and the real zebra. The discriminator here scores both of these individually, giving a higher score if it thinks its a 'real zebra'. We also need a discriminator to do this for knowing what a 'real horse' looks like as well for GAN B, hence two discriminators.
Smart guy. Vexed by the letter r. Great video all the same.
Please Please Please do a StyleGan video
How does this person have all this knowledge for all those different topics?
He watches Computerphile
Sadly there are lots of mistakes in the subtitles.
But isn’t “zebrafying” a horse a lossy process? What I mean is if you convert a brown horse to a zebra and you convert a white horse of the exact same size to a zebra they should produce the same result. But then how will the inverse function know what color to turn it back into?
Ankith Udupa I would like to know this as well. I wonder if it would try to “hide” the encoded information of the horse color in something like the placement of the stripes? But then when the inverse network is given a real Zebra that information wouldn’t be there... would it come out as a nonsense horse-color? Another commenter said a similar thing about lossy process with converting to a Monet-painting
Another commenter named Karl Kastor also mentioned something like this. They pointed me to a paper called "CycleGAN, a Master of Steganography" by Chu et al. I thought that was interesting, it used conventing aerial photos -> maps as an example of a lossy process, and showed that adding tiny imperceptible random noise to the map changed the aerial photos dramatically, so somehow it was encoding that "lost" information where it thought we wouldn't notice
One generator is predicting the input image, and the other is predicting the zebra / modified input image.
So I think whatever we are reverse-predicting is just whatever the input image is. The wrong color would have a high loss.
Are we also able to produce a okapi in the end?
Isn't that the horse from 'horsin' around'?
thank you!!!
If one were to convert to a monet painting then it would be lossy so wouldn't a cyclegan incentivise it to be a bit less like a monet so that there is enough data to covert it back to the original?
If the network gives you the same zebra every time but places the original, shrunk down and in the corner then the reverse network can just take that corner and expand it? Does that ever happen?
First question: That is probably true and depends on how you weight the two losses.
Second question: A similar thing like what you said actually happened: The network stenographically encoded the original input into the Zebra image and then the reverse network could decode this.
Karl Kastor what happens when the reverse network is given a real Zebra image? or a real Monet painting is the more interesting case. The reverse network might be looking for encoded information that isn’t there, so would it be nonsense data when it tries to interpret it?
@@AlexKnauth It might not stenographically hide the whole image, but just some parts that need to be changed. But these parts would be random if the hidden info is not there, yeah. Check out the paper "CycleGAN, a Master of Steganography" by Chu et al. for more info
@@karlkastor Oh, thank you!
I saw NASA pull out a licence plate number from a few pixels by tracking the differences between frames on a movie and back calculating what the image must have been to produce that.
Maybe combining that idea with this one can get a better 2001.
That is a deconvolution and it works if you have calibrated optics. Used in astronomy.
Didnt they use video? Interpoliate in time does increase the accuracy.
@@matsv201 Is there a relevant difference between 'movie' and 'video' that you feel is important?
@@lucidmoses yes. If its a video it can be sampled in deepth. This increase the virtual resolution quite a bit
There is software that used in cameras to take several puctueres to make s Sharp One.
@@matsv201 The main question being... How is that different then a movie.
Here's a business oppourtunity: train a GAN to covert my crappy photos into professional photos, and then become a professional photographer.
What would happen if you give it a photo that doesn't include a horse? Would it try to add zebra stripes to anything?
Probably not. There's two options, horse or zebra. The discriminator can't say "neither" so it won't try to convince it that "neither" is the wrong option.
If there's no zebra then it's likely to make the terrain look like it would be around a zebra. Like he mentioned in the video that the grass changes colour, probably because where zebras naturally live the grass is a different colour.
What prevents the two GANs from cooperating?
InputA->FalseContextButValidB->ReproducedA
The next big conspiracy theory?
Could you use a Gan like this to turn live-action movies into animated movies? Basically the reverse of a disney-remake?
The real loss needs to calculate how the fake prodused is like intended product. I.e. photo to rembrant painting style, then how well the produced painting is similar to rembrant style..
who is ROB? is there a link to his video?
Loss function? More like I II II IL ()
Nice
Is this...
First time i'm seeing mike wearing a not-so-much corporate shirt OMEGALUL
I wonder what happens if you do it with very different images, like horses to oranges back to horses, or Computerfile videos to Numberfile images back to Computerfile videos.
Joshua Hillerup check out the paper website. They do people to vegetables and back.
@@MusicBent thanks! My favourite is the face/ramen one.
Pound the like button for Dr Mike
I was wondering if you could use this time of strategy for text translation? Say spanish to english
10:08 damn you compression artifacts
could a yolo into cycle gan be a thing to smooth out the noise?
Yolo is for object detection, how would it help?
@@WhoForgot2Flush well you only want to change the horse to zebra so detect the horse and use that as input
Right?
CycleGAN is the new GucciGang
Didn't know Stingy from LazyTown was so smart!
For fixing the flickering: DVDs record the things which change between two consecutive images in a movie. Could we train an additional GAN to distinguish between such changes in normal movies (basically DVD data for real movies), perhaps even the original (style-unchanged) movie, and the changes which produce flickers in the style-changed product movies?
Dr Mike Pound + ukiyoe GAN = Steve Buscemi
Completely random, but does anyone see the similarity between Tim Urban and Dr. Mike Pound?
I was expecting you to reveal at the end of the video that the styling of your shirt was being CycleGAN-generated all along.
So we have 2 gans, because if 1 is good then 2 will be better
Surely the "F" generator is a "GG" generator
What if you could reduce images into its descriptive parts, then they could be removed from the image....each part can be compressed separately, then you have a higher ratio compression for video....and you can create internet 2.0...lol.
Take progressive scan as input. Generate interlaced video as output :-)
cool
Its pronounced zebra not zebra
Zebras are painted that way
He lets it slip @3:38. Look at that grin.
Mikey dressing for English weather 🙃
"an untrained GAN might have been better" x) xd
“An untrained GAN might have been better.” Haha I’m such a nerd.
When you hear hoof beats think horses not zebras.
Absolutely.
What's more to reach further and assume unicorn's should only be done based on pretty sound demonstrable evidence.
Evidence that at least to my knowledge hasn't been remotely detected, let alone reasonably, scientifically demonstrated.
Take oooon meeee
I guess the results could be improved a bit by adding a few free output neurons that are not part of the zebra image. These could be used by the first GAN to encode the information it destroys by turning a horse to a zebra. This could then be used by the second GAN to reconstruct a better image.
It should improve the quality of the result because it gives more freedom to the first GAN to modify the image, but not so much that it could just throw a generic zebra.
Peter Parker appears again
The video is great and all but am i the only one bothered by the sketching sound of the marker?
Maybe it will help Mass produce VR world's????
Nvidia has been looking into that. You can find TH-cam videos about it.
@@WhoForgot2Flush cool thanks
The caption is full of errors...
This seems like something that could be solved with dropout. Over fitting problem
Zero
Is tail of a zebra black and white like in video they showed? 😂
Woah, I'm early.
Honestly I am not convinced that sort of enhancement is going to ever really be applicable for legal purposes precisely because it is always going to be guesswork at least unless you are dealing with an analogue source that does in fact have fine detail that is just too difficult for humans to make out. That said I can potentially see one are these could eventually find use in police work and that is perhaps as a far cheaper and thus more accessible way to go about creating images based on eyewitness descriptions etc, currently doing this is expensive as it requires a skilled human artist to spend a lot of time in an one on one consultation with a witness if it was possible to sit the witness down and talk to a computer to help it construct an image of the suspect this could probably be used in a lot more cases than it can now. Arguably this could potentially give even less potentially contaminated results than using a human too as a human can contaminate the witness testimony subconsciously due to their body language etc a computer couldn't even unintentionally influence the witness towards a desired outcome as it has no desires of it's own let alone non verbal cues by which to let any prejudicial expectations slip though it could do nothing more or less than do it's best to produce a satisfactory match for the image the witness is trying to describe.
That part in the beginning about commissioning artists to paint pictures for the GAN as direct examples... I've long had this idea that Hollywood needs to take digital cameras, and film cameras, and have them shoot at a variety of things in tandem, then run that data through a GAN, so we produce a system to make digital footage look like film footage, and rid ourselves of all the weird digital artifacts we see in films today.
Mainly I'm just on a campaign to change the way digital photography handles bright coloured lights.
Dr. Pound shoulder shirt tug tally: 6. We need an algorithm to predict if the next video will have increasing or decreasing number of shoulder shirt tug twitches.
How different are horses and zebras? Like Nord-European humans and South-African humans? Or more, or less different?
More like human versus neanderthal I imagine. Sadly we don't have a reference genome for zebra yet, so can't calculate a genetic distance between it and equus caballus. Humans spread around the world quickly in evolutionary terms.
Can I work as a discriminator?
I have a gun!
I mean... a GAN :-)
“Zeb bra”
How to turn horses into zebras? A lot of unpaid interns and black markers!